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Flexible Shapelets Discovery for Time Series Classification

conference contribution
posted on 2020-01-01, 00:00 authored by Borui Cai Borui, Guangyan HuangGuangyan Huang, Maia Angelova TurkedjievaMaia Angelova Turkedjieva, Yong XiangYong Xiang, C H Chi
© Springer Nature Singapore Pte Ltd 2020. Time series classification is important due to its pervasive applications, especially for the emerging Smart City applications that are driven by intelligent sensors. Shapelets are sub-sequences of time series that have highly predictive abilities, and time series represented by shapelets can better reveal the patterns thus have better classification accuracy. Finding shapelets is challenging as its computational in-feasibility, most existing methods only finds shapelets with a same length or a few fixed length shapelets because the searching space of shapelets with arbitrary length is too large. In this paper, we improve the time series classification accuracy by discovering shapelets with arbitrary lengths. We borrow the idea of Apriori algorithm in association rule learning, that is, the superset shapelet candidates of a poor predictive shapelet candidate also have poor predictive abilities. Therefore, we propose a Flexible Shapelets Discovery (FSD) algorithm to discover shapelets with varying lengths. In FSD, shapelet candidates having the lower bound of length are discovered, and then we extend them into arbitrary lengths shapelets as long as their predictive abilities increases. Experiments conducted on 6 UCR time series datasets demonstrate that the arbitrary length shapelets discovered by FSD achieves better classification accuracy than those using fixed length shapelets.

History

Event

Data Science. Conference (2019 : 6th : Ningbo, China)

Volume

1179

Series

Communications in Computer and Information Science

Pagination

211 - 220

Publisher

Springer

Location

Ningbo, China

Place of publication

Berlin, Germany

Start date

2019-05-15

End date

2019-05-20

ISSN

1865-0929

eISSN

1865-0937

ISBN-13

9789811528095

Language

eng

Publication classification

E1 Full written paper - refereed

Title of proceedings

ICDS 2019 : Data science : 6th International Conference, ICDS 2019, Ningbo, China, May 15-20, 2019, revised selected papers

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